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Complexity Theory
Complexity is one dimension of every project; complexity theory helps understand the social behaviours of
teams and the networks of people involved in and around a project. The ideas apply equally to small inhouse projects as to large complicated programs - ‘complexity’ is not a synonym for complicated or large.
There are four basic dimensions to every project1:
•
Its inherent size usually measured in terms of value;
•
The degree of technical difficulty in creating the output;
•
The degree of uncertainty involved in the project; and
•
The complexity of the relationships (‘small p’ politics) both within the project team and surrounding
the project.
Interestingly, whilst all of these factors impact on the degree of difficulty associated with successfully
managing the delivery of the project, the Project Manager can only significantly influence, as opposed to
manage, the last two elements. Reducing the degree of uncertainty and enhancing the relationships with and
between project stakeholders (including the project team).
The difference between how complicated the work is and complexity theory is that managing complicated
work (ie, work with a high level of technical difficulty) is achievable by implementing appropriate systems
such as quality management and configuration management. The consequences of technical difficulty are
definable, predictable and manageable with the right people. The essence of complexity theory is that the
future is inherently unpredictable.
Highly complex (extreme) projects require a high level of expertise in stakeholder engagement to deal with
the uncertainty. This type of project cannot be fully planned because the scope and requirements evolve over
time (requiring iterative planning); but political and commercial pressures require as much certainty as
possible ‘upfront’.
Understanding Complexity Theory
Complexity theory has become a broad platform for the investigation of complex interdisciplinary situations
and can be defined as the study of how order and patterns arise from apparently chaotic systems and
conversely how complex behaviour and structures emerge from simple underlying rules. Some of the ideas
appear directly relevant to understanding project management from a relationship perspective.
The first idea is from the early days of ‘chaos theory’. The Tipping Point describes the way natural systems
can absorb influences with minimal (or predictable) change until the ‘tipping point’ is reached and then there
is a sudden catastrophic change. This idea is particularly relevant when thinking about ‘culture change’ in a
network. The social network can absorb a lot of pressure to change and targeted individuals may change
whilst under direct ‘pressure’ but the ‘cultural norms’ prevail and there is little real change until the ‘tipping
point’ is reached, then there is a sudden shift to a new set of ‘cultural norms’ and people retaining the ‘old
ideas’ are seen as being out of touch. It is impossible to predict the ‘tipping point’ until it has been reached
at least once.
The idea of Nonlinearity builds on from this. Nonlinearity suggests that you can do the same thing several
times over and get completely different results. Small differences may lead to big changes whilst big
1
For more on project size and categorisation see:
http://www.mosaicprojects.com.au/WhitePapers/WP1072_Project_Size.pdf
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variations may have minimal effect. The ‘butterfly effect’ describes the situation where minute changes in
the starting condition can have major and unpredictable consequences in non-linear systems. Importantly, all
human relationships are non-linear.
The complete unpredictability of Nonlinearity is counteracted by the idea of Strange Attractors. Strange
attractors are most easily thought of as recurring patterns that have quasi-predictable features. The behaviour
of dynamical systems in nature (eg, the weather) has a degree of predictability. However, dynamical systems
can follow a number of qualitatively different attractors depending on their initial starting condition and the
effect of external influences (see: Simulating complexity at the end of this paper).
This brings us to the concept of complex dynamical systems. These systems are continuously both receiving
and transmitting ‘energy’ to their environment, eg, a cyclone; at the detail level they are in ‘chaos’ but
overall are a quasi-predictable ‘system’. After a period of time transferring energy, these systems reach a
point of irreversible change (bifurcations) where the outcome is inherently unpredictable.
Self-organising systems are complex dynamical systems that appear capable of self-organisation and
exercising choice in a manner that makes them inherently unpredictable. Feedback loops contained within
the system ensure that ‘rich patterns’ are produced and the system itself behaves in its own unique way.
Importantly, how the system will behave cannot be determined by studying its parts. These ideas apply to
shoals of fish, ant colonies and human social groups. As these self-organising systems go about their daily
business they are continually exchanging energy and matter with their environment. This allows them to
remain in a state that is far from equilibrium and allows spontaneous behaviours and new patterns to emerge
in response to stimuli; ‘living on the edge of chaos’.
Complex adaptive systems are self-organising systems that have the capacity to learn from their experience.
This ‘system description’ appears to relate very closely to a project team, living on the ‘edge of chaos’;
responding and adapting to its surroundings (ie, the project’s stakeholders) and learning (or creating new
knowledge) as it advances. These ideas offer a new set of insights on the management of projects; the key
strand of research into complex adaptive systems that this paper will focus on is the concept of the Complex
Responsive Processes of Relating (CRPR).
CRPR puts emphasis on the interaction among people and the essentially responsive and participative nature
of the human processes of organising and relating. Organisation is an emergent property of many
individual human beings interacting together through their complex responsive processes of relating.
They use ‘language’ in conversations to simultaneously transfer information and ideas, negotiate social
status and develop power relationships. The ‘actors’ intentions, choices and actions / reactions are influenced
by and influence their conversations as they operate within the dynamic of their daily interactions with other
people. The process of ‘organising’ is the human experience emerging from the interactions between actors
who are all continual forming intentions, choosing and acting in relation to each other as they go about their
daily work together implementing the project.
The future seen from this perspective is therefore under perpetual construction by the movement of the
human action itself. Consequently, the actors, interactions and emerging organisation are located in a
specific context (the organisation’s social network, culture and ‘project team’) and are oriented towards an
‘unknown future’ (the project outcome) that the group is in the process of continually creating (or working to
achieve). In this context, the intended (or planned) future needs to be differentiated from the actual future
that unfolds over time2.
2
Complexity theory is discussed in more detail in A Simple View of ‘Complexity’ in Project Management:
http://www.mosaicprojects.com.au/Resources_Papers_070.html
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Managing within complexity
Without becoming experts in complexity theory, project managers can adapt to work within complexity.
Some of the key ideas to use in your work include:
Embrace the assumptions of complexity:
The four key assumptions within complexity are:
• All elements of a situation are interconnected.
•
Partial knowledge is all that is available.
•
Individual perceptions, thoughts and emotions are as important as actions and events.
•
Outcomes are probable, not certain.
You cannot predict or absolutely control the future, pretending you can, may feel more comfortable but is
ultimately far more dangerous than accepting a degree of unpredictability and being prepared for the
unexpected.
Understand the science of complexity is complex:
Complexity studies are themselves complex, adaptive, multi-faceted and emergent as the diagram below
demonstrates. We do not have precise or definitive solutions!
For a readable version see: http://www.art-sciencefactory.com/complexity-map_feb09.html
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Take a whole system perspective:
A broad ‘whole system’ perspective helps you define trends, overall priorities and opportunities. Avoid
becoming overloaded with information and distracted by detail. If you lead your team effectively3 they will
be motivated to look after the details.
Understand the three types of complexity:
The three types of complexity require quite different approaches:
•
Structural complexity deals with the practical issues around the number of stakeholders, their allegiances
and alliances and the practicalities of consulting and communicating with them. This type of complexity
requires a management response to ensure adequate resources are available to manage the issues.
•
Socio-political complexity deals with the attitudes and expectations of the stakeholders and how they are
likely to react to a project or a change and to each other. This type of complexity is rooted in
relationships and perceptions and requires effective leadership and communication to optimise outcomes.
•
Emergent complexity deals with changing events and the unforeseen or unknowable. Dealing with this
aspect of complexity requires an entrepreneurial approach based in flexibility and an awareness of the
emerging opportunities as well as reacting to emerging problems - many emergent situations combine
opportunities as well as threats.
Beware of assumptions and perspectives (including yours):
Careful examination of the implicit assumptions and perceptions we all take for granted is needed. Seek
diverse perspectives and apply disciplined thinking to make sure your own perceptions and assumptions are
not part of the problem.
Dig for the dynamic drivers:
The real drivers of most situations are hidden. Key factors include thoughts, perceptions and individual
motivations playing out through the network of actors in and around you project team. These factors are
frequently ‘the cause of the cause of a problem’ and can be remarkably subtle and intricate whist being
pervasive and difficult to change. Some of the factors that will influence each activity include:
Understand the power of emergence:
Complex situations are constantly evolving in unpredictable ways. An iterative approach based on making a
decision, taking action, reviewing the outcomes and deciding on the next set of actions based on what has
3
For more on leadership see: http://www.mosaicprojects.com.au/WhitePapers/WP1014_Leadership.pdf
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actually happened will move the project towards its intended goal. This cycle needs to be played out across
multiple fronts to achieve the best outcome. This agile approach to managing means the final goals may be
different to the initial expectations because all of the stakeholders have leaned new information along the
journey.
Emergence is defined as ‘the arising of novel and coherent structures, patterns and properties during the
process of self-organization in complex systems4’. The common characteristics of an emergent state are:
1. Radical novelty (features not previously observed in systems);
2. Coherence or correlation (meaning integrated wholes that maintain themselves over some period of
time);
3. A global or macro "level" (ie, there is some property of "wholeness");
4. It is the product of a dynamical process (it evolves); and
5. It is "ostensive" (it can be perceived).
The challenge is you cannot predict the emergent state until after it emerges.
Summary
Complexity theory suggests that the creation of a successful project outcome will always be an uncertain
journey, but the path to success or failure can and will be influenced by the actions and attitudes of the actors
within and around the project team. The key element is how effectively the project team uses its social
network to gather the resources (knowledge and support) needed to create success.
These ideas are closely linked to Stakeholder theory5, which suggests that a project is only successful if its
key stakeholders perceive the project to be successful. An obvious part of this requirement is the delivery to
the stakeholders of a ‘useful deliverable’, in an appropriate way, that includes elements of the ‘right output’
at the ‘right time’ for the ‘right cost’; but none of these factors are absolutes. Project success is created by
effectively managing the stakeholder’s expectations to align with what is reasonable and feasible for the
project to achieve. Managing the stakeholder’s expectations takes place through the relationships that exist
within project’s larger ‘social network’ and is ‘complex’. The emphasis for successful project managers is
shifting to a focus on influencing, motivating and leading the people who can make projects successful.
____________________
Simulating Complexity – Chaos Theory
The element of chaos theory of interest is the temporal behaviour of a system6. A system consists of parts
that interact and most business/project systems have many interdependent parts, including human beings and
even the environment. This makes it conceptually and practically difficult to pin down the behaviour of a
system under all operational conditions. It is the nonlinear elements in the system that may cause unexpected
behaviour, no matter how well we characterized the system initially. A sudden change in a nonlinear system
may give rise to chaos, but chaos is only one type of behaviour exhibited by nonlinear systems.
It is relatively easy to simulate chaos in excel using a simple formulae. An example is
4
Definition developed by economist Jeffrey Goldstein.
5
For more on stakeholders see: http://www.mosaicprojects.com.au/WhitePapers/WP1007_Stakeholder_Cycle.pdf
6
For more on systems thinking see: http://www.mosaicprojects.com.au/WhitePapers/WP1044_Systems_Thinking.pdf
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xt+1 1.9 - x2t
This is an iterative (or difference) equation, meaning that for any time-step in the future, we use the previous
value of the equation, generated at the previous time-step as the value of the time dependent variable to
generate the next value. The equation is nonlinear because it is quadratic in the time dependent term xt2.
The graph is erratic, there is no obvious pattern or cycle; but the graph was generated by a deterministic
equation, not by some random (stochastic) process. Simply by changing the precision used by Excel to
calculate the chart, differences can appear, and then disappear (then reappear later). This simple system is
also extraordinarily sensitive to the initial start value; minute changes can cause significant variation.
More complex formula in more sophisticated tools can generate fascinating charts……
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In many circumstances, chaos is a deterministic process that arises in non-linear systems. If an appropriate
model can be developed, short term predictions can be very accurate but the nature of the system makes the
prediction of long-term behaviour meaningless. And we cannot discover the history of a chaotic system by
looking at the system in its current state – mathematically such a system has equations that are noninvertible.
Managers must deal with knowing that their team is a system that will behave chaotically in a certain
circumstances. Being sensitive to this fact is an important start!
Key papers on Complexity Theory:
1. A Simple View of ‘Complexity’ in Project Management:
http://www.mosaicprojects.com.au/Resources_Papers_070.html
2. Risk Management and Complexity Theory - The Human Dimensions of Risk
http://www.mosaicprojects.com.au/Resources_Papers_072.html
3. Scheduling in the Age of Complexity
http://www.mosaicprojects.com.au/Resources_Papers_089.html
_____________________________
rd
First published 3 March 2011, augmented and updated.
This White Paper is part of Mosaic’s Project Knowledge Index
to view and download a wide range of published papers and articles see:
http://www.mosaicprojects.com.au/PM-Knowledge_Index.html
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